smousavi05/dl_seismology

This repo contains the database and supporting materials for Deep-Learning Seismology

This repository contains an updating database of machine-learning papers in Seismology used in Deep Learning Seismology paper. You can add or edit your paper info to the database if it is missing or the description is not accurate.

Seismic waves from earthquakes and other sources are used to infer the structure and properties of Earth’s interior. The availability of large-scale seismic datasets and the suitability of deep-learning techniques for seismic data processing have pushed deep learning to the forefront of fundamental, long-standing research investigations in seismology. However, some aspects of applying deep learning to seismology are likely to prove instructive for the geosciences, and perhaps other research areas more broadly. Deep learning is a powerful approach, but there are subtleties and nuances in its application. We present a systematic overview of trends, challenges, and opportunities in applications of deep-learning methods in seismology. The large amount and availability of datasets in seismology creates a great opportunity to apply machine learning and artificial intelligence to data processing. Mousavi and Beroza provide a comprehensive review of the deep-learning techniques being applied to seismic datasets, covering approaches, limitations, and opportunities. The trends in data processing and analysis can be instructive for geoscience and other research areas more broadly. —BG The ways in which deep learning can help process and analyze large seismological datasets are reviewed.

Free-Access Link to the Paper: https://shorturl.at/yNY06

article{
doi:10.1126/science.abm4470,
author = {S. Mostafa Mousavi  and Gregory C. Beroza },
title = {Deep-learning seismology},
journal = {Science},
volume = {377},
number = {6607},
pages = {eabm4470},
year = {2022},
doi = {10.1126/science.abm4470},
URL = {https://www.science.org/doi/abs/10.1126/science.abm4470},
eprint = {https://www.science.org/doi/pdf/10.1126/science.abm4470},
abstract = {}}